Skip to content
This repository has been archived by the owner on May 16, 2020. It is now read-only.

Commit

Permalink
Merge pull request #25 from matthew-humphrey/master
Browse files Browse the repository at this point in the history
* Refactored mesh-map plot generation
  • Loading branch information
TheBrigandier authored Jul 10, 2018
2 parents a2a7205 + d2a1761 commit 1035095
Show file tree
Hide file tree
Showing 4 changed files with 125 additions and 36 deletions.
159 changes: 124 additions & 35 deletions octoprint_PrusaMeshMap/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import re
import octoprint.plugin
import octoprint.printer
Expand Down Expand Up @@ -85,8 +86,8 @@ def get_update_information(self):
def mesh_level_check(self, comm, line, *args, **kwargs):
if re.match(r"^( -?\d+.\d+)+$", line):
self.mesh_level_responses.append(line)
self.mesh_level_generate()
self._logger.info("FOUND: " + line)
self.mesh_level_generate()
return line
else:
return line
Expand All @@ -96,60 +97,148 @@ def mesh_level_check(self, comm, line, *args, **kwargs):
mesh_level_responses = []

def mesh_level_generate(self):
if len(self.mesh_level_responses) == 7:

processed_responses = []
# We work with coordinates relative to the dashed line on the
# skilkscreen on the MK52 heatbed: print area coordinates. Note
# this doesn't exactly line up with the steel sheet, so we have to
# adjust for that when generating the background image, below.
# Points are measured from the middle of the PINDA / middle of the
# 4 probe circles on the MK52.

MESH_NUM_POINTS_X = 7
MESH_NUM_MEASURED_POINTS_X = 3
MESH_NUM_POINTS_Y = 7
MESH_NUM_MEASURED_POINTS_Y = 3
BED_SIZE_X = 250
BED_SIZE_Y = 210

# These values come from mesh_bed_calibration.cpp
BED_PRINT_ZERO_REF_X = 2
BED_PRINT_ZERO_REF_Y = 9.4

# Mesh probe points, in print area coordinates
# We assume points are symmetrical (i.e a rectangular grid)
MESH_FRONT_LEFT_X = 37 - BED_PRINT_ZERO_REF_X
MESH_FRONT_LEFT_Y = 18.4 - BED_PRINT_ZERO_REF_Y

MESH_REAR_RIGHT_X = 245 - BED_PRINT_ZERO_REF_X
MESH_REAR_RIGHT_Y = 210.4 - BED_PRINT_ZERO_REF_Y

# Offset of the marked print area on the steel sheet relative to
# the marked print area on the MK52. The steel sheet has margins
# outside of the print area, so we need to account for that too.

SHEET_OFFS_X = 0
# Technically SHEET_OFFS_Y is -2 (sheet is BELOW (frontward to) that on the MK52)
# However, we want to show the user a view that looks lined up with the MK52, so we
# ignore this and set the value to zero.
SHEET_OFFS_Y = 0
#
SHEET_MARGIN_LEFT = 0
SHEET_MARGIN_RIGHT = 0
# The SVG of the steel sheet (up on Github) is not symmetric as the actual one is
SHEET_MARGIN_FRONT = 17
SHEET_MARGIN_BACK = 14

sheet_left_x = -(SHEET_MARGIN_LEFT + SHEET_OFFS_X)
sheet_right_x = sheet_left_x + BED_SIZE_X + SHEET_MARGIN_LEFT + SHEET_MARGIN_RIGHT
sheet_front_y = -(SHEET_MARGIN_FRONT + SHEET_OFFS_Y)
sheet_back_y = sheet_front_y + BED_SIZE_Y + SHEET_MARGIN_FRONT + SHEET_MARGIN_BACK


mesh_range_x = MESH_REAR_RIGHT_X - MESH_FRONT_LEFT_X
mesh_range_y = MESH_REAR_RIGHT_Y - MESH_FRONT_LEFT_Y

mesh_delta_x = mesh_range_x / (MESH_NUM_POINTS_X - 1)
mesh_delta_y = mesh_range_y / (MESH_NUM_POINTS_Y - 1)

# Accumulate response lines until we have all of them
if len(self.mesh_level_responses) == MESH_NUM_POINTS_Y:

self._logger.info("Generating heatmap")

# TODO: Validate each row has MESH_NUM_POINTS_X values

mesh_values = []

# Parse response lines into a 2D array of floats in row-major order
for response in self.mesh_level_responses:
response = re.sub(r"^[ ]+", "", response)
response = re.sub(r"[ ]+", ",", response)
processed_responses.append([float(i) for i in response.split(",")])

self._logger.info(str(processed_responses));

# Let's take our list of lists and make it into
# a numpy array that matplotlib can do something
# with. We'll also take this opportunity to
# reverse the order (Y Axis). Of course, this will
# make things upside down for the user. So, we'll
# flip it again before rendering the heatmap image.
# This lets us get 0,0 at the bottom left corner.
float_array = np.array(list(reversed(processed_responses)))

# Set figure and gca objects, this will let us
# adjust things about our heatmap image as well
# as adjust axes label locations.
fig = plt.figure()
mesh_values.append([float(i) for i in response.split(",")])

# Generate a 2D array of the Z values in column-major order
col_i = 0
mesh_z = np.zeros(shape=(7,7))
for col in mesh_values:
row_i = 0
for val in col:
mesh_z[col_i][row_i] = val
row_i = row_i + 1
col_i = col_i + 1

# Calculate the X and Y values of the mesh bed points, in print area coordinates
mesh_x = np.zeros(MESH_NUM_POINTS_X)
for i in range(0, MESH_NUM_POINTS_X):
mesh_x[i] = MESH_FRONT_LEFT_X + mesh_delta_x*i

mesh_y = np.zeros(MESH_NUM_POINTS_Y)
for i in range(0, MESH_NUM_POINTS_Y):
mesh_y[i] = MESH_FRONT_LEFT_Y + mesh_delta_y*i

bed_variance = round(mesh_z.max() - mesh_z.min(), 3)

############
# Draw the heatmap
#fig = plt.figure(dpi=96, figsize=(12, 9))
fig = plt.figure(dpi=96, figsize=(10,8.3))
ax = plt.gca()

# Calculate our heatmap. Interpolation is used
# to create the smooth looks. At this point you
# can still adjust some visual elements later.
# "cmap" controls the matplotlib colormap scheme.
plt.imshow(float_array, interpolation='spline16', cmap=self._settings.get(["matplotlib_heatmap_theme"]))
# Plot all mesh points, including measured ones and the ones
# that are bogus (calculated). Indicate the actual measured
# points with a different marker.
for x_i in range(0, len(mesh_x)):
for y_i in range(0, len(mesh_y)):
if ((x_i % MESH_NUM_MEASURED_POINTS_X) == 0) and ((y_i % MESH_NUM_MEASURED_POINTS_Y) == 0):
plt.plot(mesh_x[x_i], mesh_y[y_i], 'o', color='m')
else:
plt.plot(mesh_x[x_i], mesh_y[y_i], '.', color='k')

# Draw the contour map. Y values are reversed to account for
# bottom-up orientation of plot library
contour = plt.contourf(mesh_x, mesh_y[::-1], mesh_z, alpha=.75, antialiased=True, cmap=plt.cm.get_cmap(self._settings.get(["matplotlib_heatmap_theme"])))

# Insert the background image (currently an image of the MK3 PEI-coated steel sheet)
img = mpimg.imread(self.get_asset_folder() + '/img/mk52_steel_sheet.png')
plt.imshow(img, extent=[sheet_left_x, sheet_right_x, sheet_front_y, sheet_back_y], interpolation="lanczos", cmap=plt.cm.get_cmap(self._settings.get(["matplotlib_heatmap_theme"])))

# Set axis ranges (although we don't currently show these...)
ax.set_xlim(left=sheet_left_x, right=sheet_right_x)
ax.set_ylim(bottom=sheet_front_y, top=sheet_back_y)

# Set various options about the graph image before
# we generate it. Things like labeling the axes and
# colorbar, and setting the X axis label/ticks to
# the top to better match the G81 output.
plt.title("Mesh Level: " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
plt.axis('image')
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.colorbar(label="Bed Variance: " + str(round(float_array.max() - float_array.min(), 3)) + "mm")
#ax.axes.get_xaxis().set_visible(True)
#ax.axes.get_yaxis().set_visible(True)
plt.xlabel("X Axis (mm)")
plt.ylabel("Y Axis (mm)")

# Flip that Y Axis again to put 0 at the bottom.
# Since we inverted our Y Axis above as well, this
# will also correct the view on the final heatmap.
ax.invert_yaxis()
#plt.colorbar(label="Bed Variance: " + str(round(mesh_z.max() - mesh_z.min(), 3)) + "mm")
plt.colorbar(contour, label="Measured Level (mm)")

plt.text(0.5, 0.43, "Total Bed Variance: " + str(bed_variance) + " (mm)", fontsize=10, horizontalalignment='center', verticalalignment='center', transform=ax.transAxes, bbox=dict(facecolor='#eeefff', alpha=0.5))

# Save our graph as an image in the current directory.
self._logger.info("Mesh heatmap saved to " + self.get_asset_folder() + "/img/heatmap.png")
fig.savefig(self.get_asset_folder() + '/img/heatmap.png')

fig.savefig(self.get_asset_folder() + '/img/heatmap.png', bbox_inches="tight")
self._logger.info("Heatmap updated")

del self.mesh_level_responses[:]


# If you want your plugin to be registered within OctoPrint under a different name than what you defined in setup.py
# ("OctoPrint-PluginSkeleton"), you may define that here. Same goes for the other metadata derived from setup.py that
# can be overwritten via __plugin_xyz__ control properties. See the documentation for that.
Expand Down
Binary file modified octoprint_PrusaMeshMap/static/img/heatmap.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
2 changes: 1 addition & 1 deletion setup.py
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
plugin_name = "OctoPrint-PrusaMeshMap"

# The plugin's version. Can be overwritten within OctoPrint's internal data via __plugin_version__ in the plugin module
plugin_version = "0.2.1"
plugin_version = "0.3.0"

# The plugin's description. Can be overwritten within OctoPrint's internal data via __plugin_description__ in the plugin
# module
Expand Down

0 comments on commit 1035095

Please sign in to comment.